Embeddings_builder
==================

.. class:: Embeddings_builder(self, algorithm: Literal['bert', 'count', 'tfidf'] = 'tfidf', **kwargs)

Vectorizer Wrapper class  - parent class :class:`Data`

.. list-table::
   :widths: 25 75
   :header-rows: 0

   * - Parameters
     - 
        algorithm : {"bert", "count", "tfidf"},                 default="tfidf
            which vectorizing algorithm to use:
            - 'count': CountVectorizer (default)
            - 'tfidf': TfidfVectorizer
            - 'bert': SentenceTransformer("quora-distilbert-multilingual")

        \*\*kwargs:
            additional parameters for CountVectorizer or TfidfVectorizer
        

   * - Attributes
     - 
         
        algorithm : str
            name of the used algorithm
        
       
         
        transformer : transformer instance
            transformer instance (e.g. StandardScaler)
        
       



.. raw:: html

   <h2>Example</h2>

>>> from sam_ml.data.preprocessing import Embeddings_builder
>>>
>>> model = Embeddings_builder()
>>> print(model)
Embeddings_builder()
        

.. raw:: html

   <h2>Methods</h2>

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   :header-rows: 1

   * - Method
     - Description
   
   * - :meth:`~sam_ml.data.preprocessing.embeddings.Embeddings_builder.create_parallel_bert_embeddings`
     - Function to create in parallel embeddings of given strings with bert model
   
   * - :meth:`~sam_ml.data.preprocessing.embeddings.Embeddings_builder.get_params`
     - Function to get the parameter from the transformer instance
   
   * - :meth:`~sam_ml.data.preprocessing.embeddings.Embeddings_builder.params`
     - Function to get the possible parameter values for the class
   
   * - :meth:`~sam_ml.data.preprocessing.embeddings.Embeddings_builder.set_params`
     - Function to set the parameter of the transformer instance
   
   * - :meth:`~sam_ml.data.preprocessing.embeddings.Embeddings_builder.vectorize`
     - Function to vectorize text data column
   




.. automethod:: sam_ml.data.preprocessing.embeddings.Embeddings_builder.create_parallel_bert_embeddings

.. automethod:: sam_ml.data.preprocessing.embeddings.Embeddings_builder.get_params

.. automethod:: sam_ml.data.preprocessing.embeddings.Embeddings_builder.params

.. automethod:: sam_ml.data.preprocessing.embeddings.Embeddings_builder.set_params

.. automethod:: sam_ml.data.preprocessing.embeddings.Embeddings_builder.vectorize